






"""simple docstring"""





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_SCREAMING_SNAKE_CASE   :							List[str]          =		logging.get_logger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					"""AutoTokenizer"""
    SCREAMING_SNAKE_CASE		:	str		=					["""tokenizer"""]

    SCREAMING_SNAKE_CASE		:	Dict		=					{
        """semantic_prompt""": 1,
        """coarse_prompt""": 2,
        """fine_prompt""": 2,
    }






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any=None   )	->						Optional[Any]:
      super().__init__(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		speaker_embeddings






    @classmethod
    def   UpperCamelCase			(			cls	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[str]="speaker_embeddings_path.json"			,		**__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Dict:

      if speaker_embeddings_dict_path is not None:
        lowerCamelCase_       	=		get_file_from_repo(
            __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		subfolder=kwargs.pop('subfolder'			,		__SCREAMING_SNAKE_CASE   )			,		cache_dir=kwargs.pop('cache_dir'			,		__SCREAMING_SNAKE_CASE   )			,		force_download=kwargs.pop('force_download'			,		__SCREAMING_SNAKE_CASE   )			,		proxies=kwargs.pop('proxies'			,		__SCREAMING_SNAKE_CASE   )			,		resume_download=kwargs.pop('resume_download'			,		__SCREAMING_SNAKE_CASE   )			,		local_files_only=kwargs.pop('local_files_only'			,		__SCREAMING_SNAKE_CASE   )			,		use_auth_token=kwargs.pop('use_auth_token'			,		__SCREAMING_SNAKE_CASE   )			,		revision=kwargs.pop('revision'			,		__SCREAMING_SNAKE_CASE   )			,		)
        if speaker_embeddings_path is None:
          logger.warning(
              F'''`{os.path.join(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''   )
          lowerCamelCase_       	=		None
        else:
          with open(__SCREAMING_SNAKE_CASE   ) as speaker_embeddings_json:
            lowerCamelCase_       	=		json.load(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		None

      lowerCamelCase_       	=		AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )

      return cls(tokenizer=__SCREAMING_SNAKE_CASE			,		speaker_embeddings=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Tuple="speaker_embeddings_path.json"			,		__SCREAMING_SNAKE_CASE	:				str="speaker_embeddings"			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		**__SCREAMING_SNAKE_CASE	:				int			,		)	->						Dict:
      if self.speaker_embeddings is not None:
        os.makedirs(os.path.join(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		'v2'   )			,		exist_ok=__SCREAMING_SNAKE_CASE   )

        lowerCamelCase_       	=		{}

        lowerCamelCase_       	=		save_directory

        for prompt_key in self.speaker_embeddings:
          if prompt_key != "repo_or_path":
            lowerCamelCase_       	=		self._load_voice_preset(__SCREAMING_SNAKE_CASE   )

            lowerCamelCase_       	=		{}
            for key in self.speaker_embeddings[prompt_key]:
              np.save(
                  os.path.join(
                      embeddings_dict['repo_or_path']			,		__SCREAMING_SNAKE_CASE			,		F'''{prompt_key}_{key}'''   )			,		voice_preset[key]			,		allow_pickle=__SCREAMING_SNAKE_CASE			,		)
              lowerCamelCase_       	=		os.path.join(__SCREAMING_SNAKE_CASE			,		F'''{prompt_key}_{key}.npy'''   )

            lowerCamelCase_       	=		tmp_dict

        with open(os.path.join(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )			,		'w'   ) as fp:
          json.dump(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      super().save_pretrained(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				str = None			,		**__SCREAMING_SNAKE_CASE	:				str   )	->						str:
      lowerCamelCase_       	=		self.speaker_embeddings[voice_preset]

      lowerCamelCase_       	=		{}
      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
        if key not in voice_preset_paths:
          raise ValueError(
              F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''   )

        lowerCamelCase_       	=		get_file_from_repo(
            self.speaker_embeddings.get('repo_or_path'			,		'/'   )			,		voice_preset_paths[key]			,		subfolder=kwargs.pop('subfolder'			,		__SCREAMING_SNAKE_CASE   )			,		cache_dir=kwargs.pop('cache_dir'			,		__SCREAMING_SNAKE_CASE   )			,		force_download=kwargs.pop('force_download'			,		__SCREAMING_SNAKE_CASE   )			,		proxies=kwargs.pop('proxies'			,		__SCREAMING_SNAKE_CASE   )			,		resume_download=kwargs.pop('resume_download'			,		__SCREAMING_SNAKE_CASE   )			,		local_files_only=kwargs.pop('local_files_only'			,		__SCREAMING_SNAKE_CASE   )			,		use_auth_token=kwargs.pop('use_auth_token'			,		__SCREAMING_SNAKE_CASE   )			,		revision=kwargs.pop('revision'			,		__SCREAMING_SNAKE_CASE   )			,		)
        if path is None:
          raise ValueError(
              F'''`{os.path.join(self.speaker_embeddings.get("repo_or_path"			,		"/"   )			,		voice_preset_paths[key]   )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''   )

        lowerCamelCase_       	=		np.load(__SCREAMING_SNAKE_CASE   )

      return voice_preset_dict






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[dict] = None   )	->						Dict:
      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
        if key not in voice_preset:
          raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''   )

        if not isinstance(voice_preset[key]			,		np.ndarray   ):
          raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray.'''   )

        if len(voice_preset[key].shape   ) != self.preset_shape[key]:
          raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray.'''   )






    def __call__(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		__SCREAMING_SNAKE_CASE	:				List[Any]="pt"			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=256			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=False			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=False			,		**__SCREAMING_SNAKE_CASE	:				List[str]			,		)	->						Union[str, Any]:
      if voice_preset is not None and not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        if (
            isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
            and self.speaker_embeddings is not None
            and voice_preset in self.speaker_embeddings
        ):
          lowerCamelCase_       	=		self._load_voice_preset(__SCREAMING_SNAKE_CASE   )

        else:
          if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) and not voice_preset.endswith('.npz'   ):
            lowerCamelCase_       	=		voice_preset + '.npz'

          lowerCamelCase_       	=		np.load(__SCREAMING_SNAKE_CASE   )

      if voice_preset is not None:
        self._validate_voice_preset_dict(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		BatchFeature(data=__SCREAMING_SNAKE_CASE			,		tensor_type=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.tokenizer(
          __SCREAMING_SNAKE_CASE			,		return_tensors=__SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=__SCREAMING_SNAKE_CASE			,		return_attention_mask=__SCREAMING_SNAKE_CASE			,		return_token_type_ids=__SCREAMING_SNAKE_CASE			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      if voice_preset is not None:
        lowerCamelCase_       	=		voice_preset

      return encoded_text












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						list[list[int | float]]					) ->							int:
  lowerCamelCase_       	=		len(_lowerCamelCase					)
  lowerCamelCase_       	=		len(matrix[0]					)
  lowerCamelCase_       	=		min(_lowerCamelCase						,							_lowerCamelCase					)

  for row in range(_lowerCamelCase					):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1						,							_lowerCamelCase					):
        lowerCamelCase_       	=		matrix[col][row] / matrix[row][row]
        for i in range(_lowerCamelCase						,							_lowerCamelCase					):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCamelCase_       	=		True
      for i in range(row + 1						,							_lowerCamelCase					):
        if matrix[i][row] != 0:
          lowerCamelCase_	,      lowerCamelCase_       	=		matrix[i], matrix[row]
          lowerCamelCase_       	=		False
          break
      if reduce:
        rank -= 1
        for i in range(_lowerCamelCase					):
          lowerCamelCase_       	=		matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()




